import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import datetime
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import plot
from plotly import __version__
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
print(__version__)
%matplotlib inline
df=pd.read_csv('Tata-steel.csv')
df.head(10)
5.6.0
| Date | Open Price | High Price | Low Price | Close Price | WAP | No.of Shares | No. of Trades | Total Turnover (Rs.) | Deliverable Quantity | % Deli. Qty to Traded Qty | Spread High-Low | Spread Close-Open | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 03-Jul-15 | 308.00 | 308.00 | 299.75 | 300.95 | 302.708180 | 499554 | 6430 | 151219082 | 118810.0 | 23.78 | 8.25 | -7.05 |
| 1 | 06-Jul-15 | 294.90 | 299.60 | 292.30 | 298.75 | 295.646338 | 663354 | 9184 | 196118181 | 117663.0 | 17.74 | 7.30 | 3.85 |
| 2 | 07-Jul-15 | 300.75 | 303.25 | 296.00 | 297.45 | 299.477691 | 877207 | 8581 | 262703927 | 211277.0 | 24.09 | 7.25 | -3.30 |
| 3 | 08-Jul-15 | 293.80 | 294.00 | 282.00 | 283.40 | 286.875105 | 1446189 | 20272 | 414875621 | 563505.0 | 38.96 | 12.00 | -10.40 |
| 4 | 09-Jul-15 | 285.20 | 287.25 | 279.60 | 280.55 | 282.860975 | 974983 | 13946 | 275784642 | 330248.0 | 33.87 | 7.65 | -4.65 |
| 5 | 10-Jul-15 | 282.00 | 284.60 | 279.00 | 280.95 | 281.406322 | 702436 | 9268 | 197669931 | 135447.0 | 19.28 | 5.60 | -1.05 |
| 6 | 13-Jul-15 | 281.90 | 285.60 | 279.15 | 284.45 | 282.905447 | 658957 | 8413 | 186422525 | 136197.0 | 20.67 | 6.45 | 2.55 |
| 7 | 14-Jul-15 | 285.00 | 286.90 | 281.05 | 281.70 | 283.275312 | 561904 | 7309 | 159173531 | 140665.0 | 25.03 | 5.85 | -3.30 |
| 8 | 15-Jul-15 | 283.00 | 284.90 | 279.40 | 280.70 | 281.929804 | 564323 | 7346 | 159099473 | 139267.0 | 24.68 | 5.50 | -2.30 |
| 9 | 16-Jul-15 | 285.00 | 286.75 | 280.60 | 281.50 | 282.752620 | 932539 | 10996 | 263677846 | 230537.0 | 24.72 | 6.15 | -3.50 |
# To change date in the accepted format
df['Date']=pd.to_datetime(df.Date)
df.head(10)
| Date | Open Price | High Price | Low Price | Close Price | WAP | No.of Shares | No. of Trades | Total Turnover (Rs.) | Deliverable Quantity | % Deli. Qty to Traded Qty | Spread High-Low | Spread Close-Open | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015-07-03 | 308.00 | 308.00 | 299.75 | 300.95 | 302.708180 | 499554 | 6430 | 151219082 | 118810.0 | 23.78 | 8.25 | -7.05 |
| 1 | 2015-07-06 | 294.90 | 299.60 | 292.30 | 298.75 | 295.646338 | 663354 | 9184 | 196118181 | 117663.0 | 17.74 | 7.30 | 3.85 |
| 2 | 2015-07-07 | 300.75 | 303.25 | 296.00 | 297.45 | 299.477691 | 877207 | 8581 | 262703927 | 211277.0 | 24.09 | 7.25 | -3.30 |
| 3 | 2015-07-08 | 293.80 | 294.00 | 282.00 | 283.40 | 286.875105 | 1446189 | 20272 | 414875621 | 563505.0 | 38.96 | 12.00 | -10.40 |
| 4 | 2015-07-09 | 285.20 | 287.25 | 279.60 | 280.55 | 282.860975 | 974983 | 13946 | 275784642 | 330248.0 | 33.87 | 7.65 | -4.65 |
| 5 | 2015-07-10 | 282.00 | 284.60 | 279.00 | 280.95 | 281.406322 | 702436 | 9268 | 197669931 | 135447.0 | 19.28 | 5.60 | -1.05 |
| 6 | 2015-07-13 | 281.90 | 285.60 | 279.15 | 284.45 | 282.905447 | 658957 | 8413 | 186422525 | 136197.0 | 20.67 | 6.45 | 2.55 |
| 7 | 2015-07-14 | 285.00 | 286.90 | 281.05 | 281.70 | 283.275312 | 561904 | 7309 | 159173531 | 140665.0 | 25.03 | 5.85 | -3.30 |
| 8 | 2015-07-15 | 283.00 | 284.90 | 279.40 | 280.70 | 281.929804 | 564323 | 7346 | 159099473 | 139267.0 | 24.68 | 5.50 | -2.30 |
| 9 | 2015-07-16 | 285.00 | 286.75 | 280.60 | 281.50 | 282.752620 | 932539 | 10996 | 263677846 | 230537.0 | 24.72 | 6.15 | -3.50 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1485 entries, 0 to 1484 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 1485 non-null datetime64[ns] 1 Open Price 1485 non-null float64 2 High Price 1485 non-null float64 3 Low Price 1485 non-null float64 4 Close Price 1485 non-null float64 5 WAP 1485 non-null float64 6 No.of Shares 1485 non-null int64 7 No. of Trades 1485 non-null int64 8 Total Turnover (Rs.) 1485 non-null int64 9 Deliverable Quantity 1484 non-null float64 10 % Deli. Qty to Traded Qty 1484 non-null float64 11 Spread High-Low 1485 non-null float64 12 Spread Close-Open 1485 non-null float64 dtypes: datetime64[ns](1), float64(9), int64(3) memory usage: 150.9 KB
# To check whether we have any missing Data
sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')
<AxesSubplot:>
#EDA
sns.set_palette("GnBu_d")
sns.set_style('whitegrid')
sns.jointplot(x='High Price',y='Low Price',data=df,color='blue')
<seaborn.axisgrid.JointGrid at 0x1d84e1c3ee0>
df['Open Price'].plot.hist()
<AxesSubplot:ylabel='Frequency'>
plt.style.use('ggplot')
df['Open Price'].plot.hist(alpha=0.5,bins=25,color='blue')
<AxesSubplot:ylabel='Frequency'>
df[['Open Price','Close Price','High Price','Close Price']].plot(kind='box')
<AxesSubplot:>
layout=go.Layout(
title='Stock price of Tata Steel',
xaxis=dict(
title='Date'
),
yaxis=dict(
title='Price'
)
)
df1=[{'x':df['Date'],'y':df['Close Price']}]
plot=go.Figure(data=df1,layout=layout,)
iplot(plot)
#PREDICTION BY A REGRESSION MODEL
#Building the regression MOdel
from sklearn.model_selection import train_test_split
#for preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
#For Model Evaluation
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
# Split the data into train and test sets
X=np.array(df.index).reshape(-1,1)
Y=df['Close Price']
X_train, X_test, Y_train,Y_test = train_test_split(X,Y,test_size=0.15,random_state=1001)
#feature Scaling
scaler= StandardScaler().fit(X_train)
from sklearn.linear_model import LinearRegression
#creating a linear model
lm=LinearRegression()
lm.fit(X_train,Y_train)
LinearRegression()
#Plot Actual And predicted values for train dataset
trace0 = go.Scatter(
x = X_train.T[0],
y=Y_train,
mode='markers',
name='Actual'
)
trace1 = go.Scatter(
x = X_train.T[0],
y=lm.predict(X_train).T,
mode='lines',
name='Predicted'
)
df1=[trace0,trace1]
layout.xaxis.title.text='Day'
plot2 = go.Figure(data=df1,layout=layout)
iplot(plot2)
scores =f'''
{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}
{'r2_score'.ljust(10)}{r2_score(Y_train,lm.predict(X_train))}\t{r2_score(Y_test,lm.predict(X_test))}
{'MSE'.ljust(10)}{mse(Y_train,lm.predict(X_train))}\t{mse(Y_test,lm.predict(X_test))}
'''
print(scores)
Metric Train Test r2_score 0.2113637853338285 0.28138911005029477 MSE 26976.312078813757 28115.874513353803
df1=df.reset_index()['Close Price']
#ANOTHER LINEAR REGRESSION MODEL(Approach 2)
X=df[['Open Price','High Price','Low Price','No.of Shares']]
y=df['Close Price']
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=10)
X_train.shape
(1113, 4)
from sklearn.linear_model import LinearRegression
lm=LinearRegression()
lm.fit(X_train,y_train)
LinearRegression()
# The coefficients
print('Coefficients: \n', lm.coef_)
Coefficients: [-5.91825043e-01 8.63147499e-01 7.26487405e-01 -1.80797315e-07]
predictions = lm.predict(
X_test)
plt.scatter(y_test,predictions,color='blue')
plt.xlabel('Y Test',fontsize=20,color='red')
plt.ylabel('Predicted Y',fontsize=20)
plt.plot
<function matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)>
# calculate these metrics by hand!
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))
MAE: 2.8607786203751284 MSE: 18.917985128026437 RMSE: 4.349481018239583
scores =f'''
{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}
{'r2_score'.ljust(10)}{r2_score(y_train,lm.predict(X_train))}\t{r2_score(y_test,lm.predict(X_test))}
{'MSE'.ljust(10)}{mse(y_train,lm.predict(X_train))}\t{mse(y_test,lm.predict(X_test))}
'''
print(scores)
Metric Train Test r2_score 0.9995360586342691 0.9995019419848405 MSE 15.741870667697818 18.917985128026437
#NOW WE WOLL DO PRDCTION USING TENSORFLOW AND PREDICT THE PRICE FOR NEXT 10 DAYS
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
df1=scaler.fit_transform(np.array(df1).reshape(-1,1))
print (df1)
[[0.09646022] [0.09432957] [0.09307055] ... [0.93506368] [0.93167401] [0.90513777]]
training_size=int(len(df1)*0.65)
test_size=len(df1)-training_size
train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]
training_size,test_size
(965, 520)
train_data
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import numpy
# convert an array of values into a dataset matrix
def create_dataset(dataset, time_step=1):
dataX, dataY = [], []
for i in range(len(dataset)-time_step-1):
a = dataset[i:(i+time_step), 0] ###i=0, 0,1,2,3-----99 100
dataX.append(a)
dataY.append(dataset[i + time_step, 0])
return numpy.array(dataX), numpy.array(dataY)
# reshape into X=t,t+1,t+2,t+3 and Y=t+4
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, ytest = create_dataset(test_data, time_step)
print(X_test.shape), print(ytest.shape)
(419, 100) (419,)
(None, None)
# reshape input to be [samples, time steps, features] which is required for LSTM
X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)
### Create the Stacked LSTM model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))
model.add(LSTM(50,return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam')
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 100, 50) 10400
lstm_1 (LSTM) (None, 100, 50) 20200
lstm_2 (LSTM) (None, 50) 20200
dense (Dense) (None, 1) 51
=================================================================
Total params: 50,851
Trainable params: 50,851
Non-trainable params: 0
_________________________________________________________________
model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)
Epoch 1/100 14/14 [==============================] - 7s 189ms/step - loss: 0.0216 - val_loss: 0.0306 Epoch 2/100 14/14 [==============================] - 2s 130ms/step - loss: 0.0039 - val_loss: 0.0121 Epoch 3/100 14/14 [==============================] - 2s 131ms/step - loss: 0.0017 - val_loss: 0.0052 Epoch 4/100 14/14 [==============================] - 2s 132ms/step - loss: 7.9742e-04 - val_loss: 0.0036 Epoch 5/100 14/14 [==============================] - 2s 117ms/step - loss: 7.2694e-04 - val_loss: 0.0039 Epoch 6/100 14/14 [==============================] - 2s 122ms/step - loss: 7.0373e-04 - val_loss: 0.0043 Epoch 7/100 14/14 [==============================] - 2s 121ms/step - loss: 6.7829e-04 - val_loss: 0.0043 Epoch 8/100 14/14 [==============================] - 2s 116ms/step - loss: 6.9737e-04 - val_loss: 0.0050 Epoch 9/100 14/14 [==============================] - 2s 127ms/step - loss: 6.7471e-04 - val_loss: 0.0042 Epoch 10/100 14/14 [==============================] - 2s 122ms/step - loss: 6.6030e-04 - val_loss: 0.0039 Epoch 11/100 14/14 [==============================] - 2s 132ms/step - loss: 6.3781e-04 - val_loss: 0.0042 Epoch 12/100 14/14 [==============================] - 2s 163ms/step - loss: 6.2818e-04 - val_loss: 0.0037 Epoch 13/100 14/14 [==============================] - 2s 142ms/step - loss: 6.1883e-04 - val_loss: 0.0034 Epoch 14/100 14/14 [==============================] - 2s 125ms/step - loss: 6.1132e-04 - val_loss: 0.0036 Epoch 15/100 14/14 [==============================] - 2s 120ms/step - loss: 5.9963e-04 - val_loss: 0.0034 Epoch 16/100 14/14 [==============================] - 2s 116ms/step - loss: 6.0136e-04 - val_loss: 0.0037 Epoch 17/100 14/14 [==============================] - 2s 117ms/step - loss: 6.3481e-04 - val_loss: 0.0032 Epoch 18/100 14/14 [==============================] - 2s 117ms/step - loss: 5.7008e-04 - val_loss: 0.0031 Epoch 19/100 14/14 [==============================] - 2s 117ms/step - loss: 5.7846e-04 - val_loss: 0.0033 Epoch 20/100 14/14 [==============================] - 2s 115ms/step - loss: 5.8275e-04 - val_loss: 0.0029 Epoch 21/100 14/14 [==============================] - 2s 138ms/step - loss: 5.2693e-04 - val_loss: 0.0025 Epoch 22/100 14/14 [==============================] - 2s 138ms/step - loss: 5.1990e-04 - val_loss: 0.0028 Epoch 23/100 14/14 [==============================] - 2s 155ms/step - loss: 5.0517e-04 - val_loss: 0.0026 Epoch 24/100 14/14 [==============================] - 2s 131ms/step - loss: 5.0354e-04 - val_loss: 0.0029 Epoch 25/100 14/14 [==============================] - 2s 125ms/step - loss: 5.0409e-04 - val_loss: 0.0023 Epoch 26/100 14/14 [==============================] - 2s 139ms/step - loss: 4.9685e-04 - val_loss: 0.0027 Epoch 27/100 14/14 [==============================] - 2s 132ms/step - loss: 5.3307e-04 - val_loss: 0.0024 Epoch 28/100 14/14 [==============================] - 2s 134ms/step - loss: 4.8449e-04 - val_loss: 0.0019 Epoch 29/100 14/14 [==============================] - 2s 125ms/step - loss: 4.7568e-04 - val_loss: 0.0020 Epoch 30/100 14/14 [==============================] - 2s 141ms/step - loss: 4.7949e-04 - val_loss: 0.0022 Epoch 31/100 14/14 [==============================] - 2s 133ms/step - loss: 4.5560e-04 - val_loss: 0.0017 Epoch 32/100 14/14 [==============================] - 2s 119ms/step - loss: 4.7166e-04 - val_loss: 0.0015 Epoch 33/100 14/14 [==============================] - 2s 118ms/step - loss: 4.8998e-04 - val_loss: 0.0016 Epoch 34/100 14/14 [==============================] - 2s 118ms/step - loss: 4.5278e-04 - val_loss: 0.0029 Epoch 35/100 14/14 [==============================] - 2s 134ms/step - loss: 4.6314e-04 - val_loss: 0.0027 Epoch 36/100 14/14 [==============================] - 2s 140ms/step - loss: 4.9327e-04 - val_loss: 0.0021 Epoch 37/100 14/14 [==============================] - 2s 117ms/step - loss: 4.3201e-04 - val_loss: 0.0018 Epoch 38/100 14/14 [==============================] - 2s 150ms/step - loss: 3.9871e-04 - val_loss: 0.0020 Epoch 39/100 14/14 [==============================] - 2s 137ms/step - loss: 4.1456e-04 - val_loss: 0.0020 Epoch 40/100 14/14 [==============================] - 2s 126ms/step - loss: 4.5849e-04 - val_loss: 0.0021 Epoch 41/100 14/14 [==============================] - 2s 116ms/step - loss: 4.3615e-04 - val_loss: 0.0017 Epoch 42/100 14/14 [==============================] - 2s 116ms/step - loss: 3.8359e-04 - val_loss: 0.0015 Epoch 43/100 14/14 [==============================] - 2s 116ms/step - loss: 3.5947e-04 - val_loss: 0.0015 Epoch 44/100 14/14 [==============================] - 2s 119ms/step - loss: 3.5385e-04 - val_loss: 0.0016 Epoch 45/100 14/14 [==============================] - 2s 118ms/step - loss: 3.4993e-04 - val_loss: 0.0016 Epoch 46/100 14/14 [==============================] - 2s 120ms/step - loss: 3.6205e-04 - val_loss: 0.0014 Epoch 47/100 14/14 [==============================] - 2s 117ms/step - loss: 3.2851e-04 - val_loss: 0.0013 Epoch 48/100 14/14 [==============================] - 2s 129ms/step - loss: 3.2961e-04 - val_loss: 0.0015 Epoch 49/100 14/14 [==============================] - 2s 138ms/step - loss: 3.2473e-04 - val_loss: 0.0012 Epoch 50/100 14/14 [==============================] - 2s 122ms/step - loss: 3.2092e-04 - val_loss: 0.0014 Epoch 51/100 14/14 [==============================] - 2s 132ms/step - loss: 3.1836e-04 - val_loss: 0.0016 Epoch 52/100 14/14 [==============================] - 2s 133ms/step - loss: 3.1662e-04 - val_loss: 9.9691e-04 Epoch 53/100 14/14 [==============================] - 2s 135ms/step - loss: 3.5311e-04 - val_loss: 0.0011 Epoch 54/100 14/14 [==============================] - 2s 128ms/step - loss: 3.0084e-04 - val_loss: 0.0011 Epoch 55/100 14/14 [==============================] - 2s 126ms/step - loss: 2.9835e-04 - val_loss: 0.0014 Epoch 56/100 14/14 [==============================] - 2s 125ms/step - loss: 3.0461e-04 - val_loss: 0.0012 Epoch 57/100 14/14 [==============================] - 2s 135ms/step - loss: 3.0464e-04 - val_loss: 9.2226e-04 Epoch 58/100 14/14 [==============================] - 2s 138ms/step - loss: 3.3423e-04 - val_loss: 0.0012 Epoch 59/100 14/14 [==============================] - 2s 130ms/step - loss: 2.9653e-04 - val_loss: 0.0014 Epoch 60/100 14/14 [==============================] - 2s 124ms/step - loss: 3.2118e-04 - val_loss: 0.0013 Epoch 61/100 14/14 [==============================] - 2s 129ms/step - loss: 2.8639e-04 - val_loss: 0.0012 Epoch 62/100 14/14 [==============================] - 2s 118ms/step - loss: 2.7868e-04 - val_loss: 0.0010 Epoch 63/100 14/14 [==============================] - 2s 133ms/step - loss: 3.0204e-04 - val_loss: 0.0012 Epoch 64/100 14/14 [==============================] - 2s 143ms/step - loss: 3.1422e-04 - val_loss: 0.0015 Epoch 65/100 14/14 [==============================] - 2s 128ms/step - loss: 3.4381e-04 - val_loss: 0.0013 Epoch 66/100 14/14 [==============================] - 2s 154ms/step - loss: 2.7934e-04 - val_loss: 8.5070e-04 Epoch 67/100 14/14 [==============================] - 2s 142ms/step - loss: 2.8754e-04 - val_loss: 9.2565e-04 Epoch 68/100 14/14 [==============================] - 2s 125ms/step - loss: 2.7482e-04 - val_loss: 0.0012 Epoch 69/100 14/14 [==============================] - 2s 137ms/step - loss: 2.8165e-04 - val_loss: 0.0012 Epoch 70/100 14/14 [==============================] - 2s 130ms/step - loss: 2.8345e-04 - val_loss: 0.0016 Epoch 71/100 14/14 [==============================] - 2s 126ms/step - loss: 2.9024e-04 - val_loss: 7.6359e-04 Epoch 72/100 14/14 [==============================] - 2s 126ms/step - loss: 2.6435e-04 - val_loss: 9.8066e-04 Epoch 73/100 14/14 [==============================] - 2s 127ms/step - loss: 2.5267e-04 - val_loss: 7.1246e-04 Epoch 74/100 14/14 [==============================] - 2s 144ms/step - loss: 2.8395e-04 - val_loss: 7.7438e-04 Epoch 75/100 14/14 [==============================] - 2s 137ms/step - loss: 2.6123e-04 - val_loss: 8.8154e-04 Epoch 76/100 14/14 [==============================] - 2s 130ms/step - loss: 2.4242e-04 - val_loss: 8.9714e-04 Epoch 77/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4386e-04 - val_loss: 0.0011 Epoch 78/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4902e-04 - val_loss: 8.1598e-04 Epoch 79/100 14/14 [==============================] - 2s 119ms/step - loss: 2.4013e-04 - val_loss: 6.7061e-04 Epoch 80/100 14/14 [==============================] - 2s 119ms/step - loss: 3.1375e-04 - val_loss: 0.0012 Epoch 81/100 14/14 [==============================] - 2s 120ms/step - loss: 3.0737e-04 - val_loss: 0.0014 Epoch 82/100 14/14 [==============================] - 2s 119ms/step - loss: 2.5192e-04 - val_loss: 0.0011 Epoch 83/100 14/14 [==============================] - 2s 137ms/step - loss: 2.4572e-04 - val_loss: 0.0010 Epoch 84/100 14/14 [==============================] - 2s 155ms/step - loss: 2.5816e-04 - val_loss: 7.1704e-04 Epoch 85/100 14/14 [==============================] - 2s 138ms/step - loss: 2.3946e-04 - val_loss: 9.8093e-04 Epoch 86/100 14/14 [==============================] - 2s 133ms/step - loss: 2.3071e-04 - val_loss: 6.9482e-04 Epoch 87/100 14/14 [==============================] - 2s 129ms/step - loss: 2.5062e-04 - val_loss: 6.5913e-04 Epoch 88/100 14/14 [==============================] - 2s 123ms/step - loss: 2.4183e-04 - val_loss: 6.9999e-04 Epoch 89/100 14/14 [==============================] - 2s 121ms/step - loss: 2.4447e-04 - val_loss: 0.0013 Epoch 90/100 14/14 [==============================] - 2s 121ms/step - loss: 2.3850e-04 - val_loss: 7.0406e-04 Epoch 91/100 14/14 [==============================] - 2s 124ms/step - loss: 2.4112e-04 - val_loss: 0.0011 Epoch 92/100 14/14 [==============================] - 2s 133ms/step - loss: 2.3137e-04 - val_loss: 7.6536e-04 Epoch 93/100 14/14 [==============================] - 2s 135ms/step - loss: 2.6959e-04 - val_loss: 6.2377e-04 Epoch 94/100 14/14 [==============================] - 2s 140ms/step - loss: 2.3121e-04 - val_loss: 5.7497e-04 Epoch 95/100 14/14 [==============================] - 2s 128ms/step - loss: 2.3352e-04 - val_loss: 7.8791e-04 Epoch 96/100 14/14 [==============================] - 2s 128ms/step - loss: 2.1362e-04 - val_loss: 7.8944e-04 Epoch 97/100 14/14 [==============================] - 2s 121ms/step - loss: 2.1115e-04 - val_loss: 0.0010 Epoch 98/100 14/14 [==============================] - 2s 122ms/step - loss: 2.2986e-04 - val_loss: 5.8679e-04 Epoch 99/100 14/14 [==============================] - 2s 122ms/step - loss: 2.0538e-04 - val_loss: 7.3830e-04 Epoch 100/100 14/14 [==============================] - 2s 132ms/step - loss: 2.0567e-04 - val_loss: 6.0433e-04
<keras.callbacks.History at 0x1d857546d90>
import tensorflow as tf
tf.__version__
'2.12.0'
### Lets Do the prediction and check performance metrics
train_predict=model.predict(X_train)
test_predict=model.predict(X_test)
27/27 [==============================] - 2s 29ms/step 14/14 [==============================] - 1s 27ms/step
##Transformback to original form
train_predict=scaler.inverse_transform(train_predict)
test_predict=scaler.inverse_transform(test_predict)
### Calculate RMSE performance metrics
import math
from sklearn.metrics import mean_squared_error
math.sqrt(mean_squared_error(y_train,train_predict))
0.014140821522976035
### Test Data RMSE
math.sqrt(mean_squared_error(ytest,test_predict))
0.02458303470611413
### Plotting
# shift train predictions for plotting
look_back=100
trainPredictPlot = numpy.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(df1)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(df1),color='red')
plt.plot(trainPredictPlot,color='blue')
plt.plot(testPredictPlot,color='yellow')
plt.xlabel('Days',fontsize=18)
plt.ylabel('Price In rupees',fontsize=20)
plt.legend(["Original Price","train price","test price"],loc="upper left")
<matplotlib.legend.Legend at 0x1d8670c30a0>
len(test_data)
520
x_input=test_data[420:].reshape(1,-1)
x_input.shape
(1, 100)
temp_input=list(x_input)
temp_input=temp_input[0].tolist()
temp_input
[0.46854873855987594, 0.4857391893855018, 0.48249479444094706, 0.473487966684422, 0.4776039901215437, 0.4639969008764709, 0.4556680063919422, 0.48157474214323753, 0.4797346375478185, 0.48084838506609845, 0.45450583506851955, 0.4670476006004551, 0.5115006537213693, 0.5116459251367971, 0.5244782334995883, 0.49726405500944254, 0.5121785869933659, 0.5170209675076267, 0.5572127257759913, 0.5391990702629412, 0.5151808629122074, 0.5191031911287589, 0.48932255096605487, 0.5063677303762528, 0.5021548593288461, 0.5178441721950509, 0.5062224589608251, 0.4871919035397801, 0.48772456539634884, 0.5148903200813519, 0.5250593191612996, 0.5225897050990267, 0.48622342743692787, 0.505108711442545, 0.5474795409423272, 0.5795360999467336, 0.5913515084015302, 0.6406469420367051, 0.6450535083046824, 0.6406469420367051, 0.6521718076606458, 0.6937194324730036, 0.6758510483753812, 0.6295094668539052, 0.656239407292625, 0.6745436056365308, 0.6665536777880006, 0.6639872161154423, 0.6711055154714056, 0.6971575226381288, 0.7010314270495375, 0.7157522638128904, 0.7513437605927071, 0.7454360563653091, 0.8040772843930073, 0.80635320323471, 0.8359885719819862, 0.8355043339305601, 0.8402498668345357, 0.870853711684664, 0.9500750568979708, 0.9831969396155149, 1.0, 0.9464916953174181, 0.9014091327296496, 0.9193743644375574, 0.9473149000048422, 0.9325940632414893, 0.8750181589269284, 0.8824270011137474, 0.8624279695898502, 0.8750665827320712, 0.8527432085613285, 0.8670282310783979, 0.8739528352137911, 0.8951624618662535, 0.870611592658951, 0.8931286620502636, 0.8913369812599874, 0.8895937242748535, 0.8977289235388115, 0.8804900489080432, 0.8711926783206625, 0.8844123771245942, 0.9275095637015154, 0.9331267250980579, 0.9424240956854388, 0.911142317563314, 0.8738559876035059, 0.8618953077332816, 0.8756476683937822, 0.8834439010217421, 0.870127354607525, 0.8831533581908866, 0.9332719965134859, 0.9517214662728195, 0.9404871434797346, 0.9350636773037624, 0.9316740109437798, 0.9051377657256305]
# demonstrate prediction for next 10 days
from numpy import array
lst_output=[]
n_steps=100
i=0
while(i<30):
if(len(temp_input)>100):
#print(temp_input)
x_input=np.array(temp_input[1:])
print("{} day input {}".format(i,x_input))
x_input=x_input.reshape(1,-1)
x_input = x_input.reshape((1, n_steps, 1))
#print(x_input)
yhat = model.predict(x_input, verbose=0)
print("{} day output {}".format(i,yhat))
temp_input.extend(yhat[0].tolist())
temp_input=temp_input[1:]
#print(temp_input)
lst_output.extend(yhat.tolist())
i=i+1
else:
x_input = x_input.reshape((1, n_steps,1))
yhat = model.predict(x_input, verbose=0)
print(yhat[0])
temp_input.extend(yhat[0].tolist())
print(len(temp_input))
lst_output.extend(yhat.tolist())
i=i+1
print(lst_output)
[0.8919715] 101 1 day input [0.48573919 0.48249479 0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153] 1 day output [[0.87675655]] 2 day input [0.48249479 0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655] 2 day output [[0.86094797]] 3 day input [0.47348797 0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797] 3 day output [[0.84559965]] 4 day input [0.47760399 0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965] 4 day output [[0.8310815]] 5 day input [0.4639969 0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151] 5 day output [[0.8175638]] 6 day input [0.45566801 0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377] 6 day output [[0.8051158]] 7 day input [0.48157474 0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582] 7 day output [[0.7937276]] 8 day input [0.47973464 0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758] 8 day output [[0.78332597]] 9 day input [0.48084839 0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597] 9 day output [[0.77379394]] 10 day input [0.45450584 0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394] 10 day output [[0.76499057]] 11 day input [0.4670476 0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057] 11 day output [[0.7567651]] 12 day input [0.51150065 0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513] 12 day output [[0.7489709]] 13 day input [0.51164593 0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093] 13 day output [[0.74147487]] 14 day input [0.52447823 0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487] 14 day output [[0.73416233]] 15 day input [0.49726406 0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233] 15 day output [[0.72694296]] 16 day input [0.51217859 0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296] 16 day output [[0.7197505]] 17 day input [0.51702097 0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052] 17 day output [[0.71254283]] 18 day input [0.55721273 0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283] 18 day output [[0.7053]] 19 day input [0.53919907 0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997] 19 day output [[0.698021]] 20 day input [0.51518086 0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099] 20 day output [[0.69071895]] 21 day input [0.51910319 0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895] 21 day output [[0.68341875]] 22 day input [0.48932255 0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875] 22 day output [[0.67615265]] 23 day input [0.50636773 0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265] 23 day output [[0.66895497]] 24 day input [0.50215486 0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497] 24 day output [[0.66186106]] 25 day input [0.51784417 0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106] 25 day output [[0.65490395]] 26 day input [0.50622246 0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395] 26 day output [[0.6481117]] 27 day input [0.4871919 0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 ] 27 day output [[0.6415083]] 28 day input [0.48772457 0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 0.64150828] 28 day output [[0.6351101]] 29 day input [0.51489032 0.52505932 0.52258971 0.48622343 0.50510871 0.54747954 0.5795361 0.59135151 0.64064694 0.64505351 0.64064694 0.65217181 0.69371943 0.67585105 0.62950947 0.65623941 0.67454361 0.66655368 0.66398722 0.67110552 0.69715752 0.70103143 0.71575226 0.75134376 0.74543606 0.80407728 0.8063532 0.83598857 0.83550433 0.84024987 0.87085371 0.95007506 0.98319694 1. 0.9464917 0.90140913 0.91937436 0.9473149 0.93259406 0.87501816 0.882427 0.86242797 0.87506658 0.85274321 0.86702823 0.87395284 0.89516246 0.87061159 0.89312866 0.89133698 0.88959372 0.89772892 0.88049005 0.87119268 0.88441238 0.92750956 0.93312673 0.9424241 0.91114232 0.87385599 0.86189531 0.87564767 0.8834439 0.87012735 0.88315336 0.933272 0.95172147 0.94048714 0.93506368 0.93167401 0.90513777 0.89197153 0.87675655 0.86094797 0.84559965 0.83108151 0.81756377 0.80511582 0.79372758 0.78332597 0.77379394 0.76499057 0.75676513 0.74897093 0.74147487 0.73416233 0.72694296 0.71975052 0.71254283 0.70529997 0.69802099 0.69071895 0.68341875 0.67615265 0.66895497 0.66186106 0.65490395 0.6481117 0.64150828 0.63511008] 29 day output [[0.6289286]] [[0.8919715285301208], [0.8767565488815308], [0.8609479665756226], [0.8455996513366699], [0.8310815095901489], [0.8175637722015381], [0.805115818977356], [0.7937275767326355], [0.7833259701728821], [0.7737939357757568], [0.7649905681610107], [0.7567651271820068], [0.7489709258079529], [0.7414748668670654], [0.7341623306274414], [0.7269429564476013], [0.7197505235671997], [0.7125428318977356], [0.705299973487854], [0.6980209946632385], [0.690718948841095], [0.6834187507629395], [0.6761526465415955], [0.6689549684524536], [0.6618610620498657], [0.6549039483070374], [0.6481117010116577], [0.6415082812309265], [0.635110080242157], [0.6289286017417908]]
day_new=np.arange(1,101)
day_pred=np.arange(101,131)
len(df1)
1485
plt.plot(day_new,scaler.inverse_transform(df1[1385:]),color='red')
plt.plot(day_pred,scaler.inverse_transform(lst_output),color='blue')
plt.xlabel('Days',fontsize=20,color='black')
plt.ylabel('Price In rupees',fontsize=20,color='black')
plt.legend(["Original Price","predicted price"],loc="lower right") #predicted rice for next 10 days
<matplotlib.legend.Legend at 0x1d866e378b0>
df3=df1.tolist()
df3.extend(lst_output)
plt.plot(df3[1400:],color='black')
[<matplotlib.lines.Line2D at 0x1d866cebeb0>]
df3=scaler.inverse_transform(df3).tolist()
plt.plot(df3,color='blue')
plt.xlabel('Days',fontsize=20)
plt.ylabel('Price In rupees',fontsize=20)
Text(0, 0.5, 'Price In rupees')